vllm.models.deepseek_v4.nvidia.dspark ¶

class DSparkDeepseekV4ForCausalLM(nn.Module):
    # Draft weights ship in the target checkpoint (mtp.*) without embed/head, so
    # load_dspark_model always aliases the target's.
    has_own_embed_tokens = False
    has_own_lm_head = False
    # Full-vocab draft: draft ids are target ids, no remapping needed.
    draft_id_to_target_id = None

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        assert vllm_config.speculative_config is not None
        self.draft_model_config = vllm_config.speculative_config.draft_model_config
        self.config = self.draft_model_config.hf_config
        self.model = DSparkDeepseekV4Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        # Shared with the target (aliased by the speculator's load utility).
        self.lm_head = ParallelLMHead(
            self.config.vocab_size,
            self.config.hidden_size,
            prefix=maybe_prefix(prefix, "lm_head"),
        )
        self.logits_processor = LogitsProcessor(self.config.vocab_size)

    # --- Hooks used by the speculator -------------------------------------

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.embed_input_ids(input_ids)

    def combine_hidden_states(self, aux_hidden_states: torch.Tensor) -> torch.Tensor:
        return self.model.combine_hidden_states(aux_hidden_states)

    def get_draft_kv_cache_layer_names(self) -> list[str]:
        # DSV4 MLA path: each draft layer's sliding-window cache is a separate
        # layer, named by its prefix.
        return [layer.attn.swa_cache_layer.prefix for layer in self.model.layers]

    def precompute_and_store_context_kv(
        self,
        context_states: torch.Tensor,
        context_positions: torch.Tensor,
        context_slot_mappings: list[torch.Tensor | None] | None = None,
    ) -> None:
        self.model.precompute_and_store_context_kv(
            context_states, context_positions, context_slot_mappings
        )

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        # Returns the pre-norm hc_head hidden ([T, hidden_size]).
        return self.model(input_ids, positions, inputs_embeds)

    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """Base logits U_k = lm_head(norm(head_hidden))."""
        return self.logits_processor(self.lm_head, self.model.norm(hidden_states))

    def compute_draft_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # Full-vocab draft: base logits, no d2t scatter.
        return self.compute_logits(hidden_states)

    def map_draft_to_target(self, draft_ids: torch.Tensor) -> torch.Tensor:
        return draft_ids  # full-vocab: draft ids are target ids

    def markov_embed(self, token_ids: torch.Tensor) -> torch.Tensor:
        return self.model.markov_head.embed(token_ids)

    def markov_bias(self, markov_embed: torch.Tensor) -> torch.Tensor:
        return self.model.markov_head.bias(markov_embed, self.logits_processor)

    # --- Weight loading ----------------------------------------------------

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        """Load the ``mtp.{0,1,2}.*`` draft weights from the target checkpoint.

        Non-mtp weights (embed/head/main layers) belong to the target model and
        are skipped here. ``embed_tokens``/``lm_head`` are aliased from the target.
        """
        first_layer = self.model.layers[0]
        use_mega_moe = first_layer.ffn.use_mega_moe
        if use_mega_moe:
            expert_mapping = make_deepseek_v4_expert_params_mapping(
                self.config.n_routed_experts
            )
        else:
            expert_mapping = fused_moe_make_expert_params_mapping(
                self,
                ckpt_gate_proj_name="w1",
                ckpt_down_proj_name="w2",
                ckpt_up_proj_name="w3",
                num_experts=self.config.n_routed_experts,
            )
        expert_scale_suffix = (
            ".weight_scale"
            if getattr(self.config, "expert_dtype", "fp4") == "fp4"
            else ".weight_scale_inv"
        )

        # (param_name, ckpt_shard_name, shard_id) for non-expert stacked params.
        stacked_params_mapping = [
            ("gate_up_proj", "w1", 0),
            ("gate_up_proj", "w3", 1),
            ("attn.fused_wqa_wkv", "attn.wq_a", 0),
            ("attn.fused_wqa_wkv", "attn.wkv", 1),
        ]

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()

        tp_size = get_tensor_model_parallel_world_size()
        tp_rank = get_tensor_model_parallel_rank()
        n_local_head = self.config.num_attention_heads // tp_size
        head_start = n_local_head * tp_rank
        head_end = n_local_head * (tp_rank + 1)

        for name, loaded_weight in weights:
            mapped = self._remap_dspark_name(name)
            if mapped is None:
                continue
            name = mapped

            # ``.scale`` -> per-method scale suffix.
            if name.endswith(".scale"):
                suffix = (
                    expert_scale_suffix
                    if _EXPERT_SCALE_RE.search(name)
                    else ".weight_scale_inv"
                )
                name = name.removesuffix(".scale") + suffix

            # E8M0 expert scales: keep raw exponent bytes.
            if ".experts." in name:
                if (
                    "weight_scale" in name
                    and loaded_weight.dtype == torch.float8_e8m0fnu
                ):
                    loaded_weight = loaded_weight.view(torch.uint8)
                for param_name, weight_name, expert_id, shard_id in expert_mapping:
                    if weight_name not in name:
                        continue
                    name_mapped = name.replace(weight_name, param_name)
                    param = params_dict[name_mapped]
                    success = param.weight_loader(
                        param,
                        loaded_weight,
                        name_mapped,
                        shard_id=shard_id,
                        expert_id=expert_id,
                        return_success=True,
                    )
                    if success:
                        loaded_params.add(name_mapped)
                        break
                continue

            # Stacked rules only apply to decoder-layer weights. Head-stack params
            # (main_proj/norm/hc_head/markov_head) load directly — otherwise e.g.
            # "markov_w1" would collide with the "w1" shard rule.
            is_layer_param = name.startswith("model.layers.")
            for param_name, weight_name, stacked_shard_id in stacked_params_mapping:
                if not is_layer_param or weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                param.weight_loader(param, loaded_weight, stacked_shard_id)
                loaded_params.add(name)
                break
            else:
                if "attn_sink" in name:
                    narrow = loaded_weight[head_start:head_end]
                    params_dict[name][: narrow.shape[0]].copy_(narrow)
                    loaded_params.add(name)
                    continue
                if ".shared_experts.w2" in name:
                    name = name.replace(
                        ".shared_experts.w2", ".shared_experts.down_proj"
                    )
                if name.endswith(".ffn.gate.bias"):
                    name = name.replace(
                        ".ffn.gate.bias", ".ffn.gate.e_score_correction_bias"
                    )
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
                loaded_params.add(name)

        self._finalize_moe()
        logger.info_once("DSpark draft model loaded: %d params", len(loaded_params))
        return loaded_params

    def _finalize_moe(self) -> None:
        for layer in self.model.layers:
            layer.ffn.finalize_mega_moe_weights()

    def _remap_dspark_name(self, name: str) -> str | None:
        """Map a checkpoint ``mtp.{i}.*`` name to this model's parameter path.

        Returns None for non-mtp weights (owned by the target model).
        """
        m = re.match(r"mtp\.(\d+)\.(.*)", name)
        if m is None:
            return None
        stage = int(m.group(1))
        rest = m.group(2)
        # The confidence head is not wired into inference yet; drop its weights.
        if rest.startswith("confidence_head."):
            return None
        # Head-stack params live at model level (mtp.last), context combiner at
        # model level (mtp.0); everything else is a per-layer decoder block.
        head_prefixes = (
            "norm.",
            "hc_head_fn",
            "hc_head_base",
            "hc_head_scale",
            "markov_head.",
        )
        if rest.startswith(("main_proj.", "main_norm.")) or rest.startswith(
            head_prefixes
        ):
            return f"model.{rest}"
        return f"model.layers.{stage}.{rest}"

class DSparkDeepseekV4Model(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()
        assert vllm_config.speculative_config is not None
        config = vllm_config.speculative_config.draft_model_config.hf_config
        self.config = config
        self.hidden_size = config.hidden_size
        self.hc_mult = config.hc_mult
        self.hc_eps = config.hc_eps
        self.rms_norm_eps = config.rms_norm_eps
        self.num_hidden_layers = config.num_hidden_layers
        self.target_layer_ids = tuple(config.dspark_target_layer_ids)

        self.num_dspark_layers = getattr(config, "n_mtp_layers", None) or 3

        # Shared with the target (aliased by the speculator's loading utility).
        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            prefix=maybe_prefix(prefix, "embed_tokens"),
        )

        self.main_proj = ReplicatedLinear(
            config.hidden_size * len(self.target_layer_ids),
            config.hidden_size,
            bias=False,
            return_bias=False,
            quant_config=vllm_config.quant_config,
            prefix=maybe_prefix(prefix, "main_proj"),
        )
        self.main_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        current_vllm_config = get_current_vllm_config()
        self.layers = nn.ModuleList(
            [
                DeepseekV4DecoderLayer(
                    current_vllm_config,
                    prefix=maybe_prefix(prefix, f"layers.{self.num_hidden_layers + i}"),
                )
                for i in range(self.num_dspark_layers)
            ]
        )

        # Heads: final norm + hc_head, and the Markov head
        # Loaded from the "final" MTP layer weights (mtp.*) in the target checkpoint
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        hc_dim = self.hc_mult * config.hidden_size
        self.hc_head_fn = nn.Parameter(
            torch.empty(self.hc_mult, hc_dim, dtype=torch.float32),
            requires_grad=False,
        )
        self.hc_head_base = nn.Parameter(
            torch.empty(self.hc_mult, dtype=torch.float32), requires_grad=False
        )
        self.hc_head_scale = nn.Parameter(
            torch.empty(1, dtype=torch.float32), requires_grad=False
        )
        draft_vocab_size = (
            getattr(config, "draft_vocab_size", None) or config.vocab_size
        )
        self.markov_head = DSparkMarkovHead(
            config.vocab_size,
            draft_vocab_size,
            config.dspark_markov_rank,
            prefix=maybe_prefix(prefix, "markov_head"),
        )

    def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

    def combine_hidden_states(self, aux_hidden_states: torch.Tensor) -> torch.Tensor:
        """main_x = main_norm(main_proj(concat of target aux hidden states)).

        ``aux_hidden_states`` is [T, hidden_size * len(target_layer_ids)].
        """
        return self.main_norm(self.main_proj(aux_hidden_states))

    @torch.inference_mode()
    def precompute_and_store_context_kv(
        self,
        main_x: torch.Tensor,
        context_positions: torch.Tensor,
        context_slot_mappings: list[torch.Tensor | None] | None = None,
    ) -> None:
        """Insert the sliding-window context KV for every draft layer.

        Mirrors the reference DSparkAttention: each layer derives its context KV
        from the SAME projected target hidden ``main_x``, via that layer's own
        ``wkv`` + ``kv_norm`` + RoPE + quant, then writes it at the
        layer's context slots.

        ``context_slot_mappings`` is a per-layer list (each entry is the context
        slot mapping for that layer's kv-cache group, since the hybrid manager may
        place draft layers in different groups). ``None`` (or a ``None`` entry)
        runs the projection to reserve workspace but writes nothing (profiling).
        """
        for i, layer in enumerate(self.layers):
            slot_mapping = (
                None if context_slot_mappings is None else context_slot_mappings[i]
            )
            attn = layer.attn
            # Optimized DSV4 MLA path: wkv part of the fused wq_a|wkv projection
            # (q_lora part discarded), then RoPE/quant/insert via the fused op.
            qr_kv, _ = attn.fused_wqa_wkv(main_x)
            kv = qr_kv[..., attn.q_lora_rank :]
            kv = attn.kv_norm(kv)
            if slot_mapping is None:
                continue
            _insert_context_kv(attn, kv, context_positions, slot_mapping)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        inputs_embeds: torch.Tensor | None = None,
    ) -> torch.Tensor:
        if inputs_embeds is None:
            inputs_embeds = self.embed_input_ids(input_ids)
        # Expand to hc_mult copies for hyper-connections ([T, H] -> [T, hc, H]).
        hidden_states = inputs_embeds.unsqueeze(-2).repeat(1, self.hc_mult, 1)

        residual = post_mix = res_mix = None
        for layer in self.layers:
            hidden_states, residual, post_mix, res_mix = layer(
                hidden_states,
                positions,
                input_ids,
                post_mix,
                res_mix,
                residual,
            )
        hidden_states = mhc_post_tilelang(hidden_states, residual, post_mix, res_mix)
        # hc_head reduces the hc copies; return the PRE-norm head hidden
        hidden_states = hc_head_fused_kernel_tilelang(
            hidden_states,
            self.hc_head_fn,
            self.hc_head_scale,
            self.hc_head_base,
            self.rms_norm_eps,
            self.hc_eps,
        )
        return hidden_states